ArticleIEEE transactions on bio-medical engineering2017
Dynamic Estimation of the Auditory Temporal Response Function From MEG in Competing-Speaker Environments.
Article in IEEE transactions on bio-medical engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- "When the word ends, the story begins": word offsets improve encoding of natural speech in a MEG study.Brain topography · 2026Article
- Very Early Cortical Auditory Responses to Speech in Humans.bioRxiv : the preprint server for biology · 2026Article
- Enhanced Time-Locked Decoding for Spoken Words but Not Environmental Sounds in Natural-Like Auditory Conditions.The European journal of neuroscience · 2026Article
- Hybrid channel attention network for auditory attention detection.Scientific reports · 2025Article
- Attention, musicality, and familiarity shape cortical speech tracking at the musical cocktail party.Brain and language · 2025Article
- VARX Granger analysis: Models for neuroscience, physiology, sociology and econometrics.PloS one · 2025Article
- Attention to audiovisual speech shapes neural processing through feedback-feedforward loops between different nodes of the speech network.PLoS biology · 2024Article
- Improving auditory attention decoding by classifying intracranial responses to glimpsed and masked acoustic events.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Algorithms for Estimating Time-Locked Neural Response Components in Cortical Processing of Continuous Speech.IEEE transactions on bio-medical engineering · 2023Article
- Article
- A Speech-Level-Based Segmented Model to Decode the Dynamic Auditory Attention States in the Competing Speaker Scenes.Frontiers in neuroscience · 2021Article
- Behavioral Account of Attended Stream Enhances Neural Tracking.Frontiers in neuroscience · 2021Article
- Dynamic estimation of auditory temporal response functions via state-space models with Gaussian mixture process noise.PLoS computational biology · 2020Article
- Neuro-current response functions: A unified approach to MEG source analysis under the continuous stimuli paradigm.NeuroImage · 2020Article
- Article
- Real-Time Tracking of Magnetoencephalographic Neuromarkers during a Dynamic Attention-Switching Task.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2019Article
- A Tutorial on Auditory Attention Identification Methods.Frontiers in neuroscience · 2019Article
- Neural source dynamics of brain responses to continuous stimuli: Speech processing from acoustics to comprehension.NeuroImage · 2018Article
- Real-Time Tracking of Selective Auditory Attention From M/EEG: A Bayesian Filtering Approach.Frontiers in neuroscience · 2018Article
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3 authors.
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Abstract
objectiveA central problem in computational neuroscience is to characterize brain function using neural activity recorded from the brain in response to sensory inputs with statistical confidence. Most of existing estimation techniques, such as those based on reverse correlation, exhibit two main limitations: first, they are unable to produce dynamic estimates of the neural activity at a resolution comparable with that of the recorded data, and second, they often require heavy averaging across time as well as multiple trials in order to construct statistical confidence intervals for a precise interpretation of data. In this paper, we address the above-mentioned issues for estimating auditory temporal response function (TRF) as a parametric computational model for selective auditory attention in competing-speaker environments.
methodsThe TRF is a sparse kernel which regresses auditory MEG data with respect to the envelopes of the speech streams. We develop an efficient estimation technique by exploiting the sparsity of the TRF and adopting an ℓ
resultsWe evaluate the performance of our proposed estimator using evoked MEG responses from the human brain in an auditory attention experiment with two competing speakers. The TRFs are estimated dynamically over time using the proposed technique with multisecond resolution, which is a significant improvement over previous results with a temporal resolution of the order of a minute.
conclusionApplication of our method to MEG data reveals a precise characterization of the modulation of M50 and M100 evoked responses with respect to the attentional state of the subject at multisecond resolution. SIGNIFICANCE: Our proposed estimation technique provides a high resolution real-time attention decoding framework in multispeaker environments with potential application in smart hearing aid technology.
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